Background.
Early Classification algorithms classify time series before observing every timestep [1]}.
These powerful methods give users enough time to react in time-sensitive domains.
For example, early warning systems help clinicians improve their patients' outcomes [2]}, [3]}.
Still, the best time to classify depends on the task and a given time series because signals are often sporadic.
Too early and it may be a false alarm, fatiguing the end-user [4]}.
Too late and the user cannot react.
An ideal classification is both early and accurate, but these goals conflict: an early classification may ignore relevant future data, as illustrated in Figure REF .
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